One intelligence layer across industrial operations.
Arvane brings together operational, energy, equipment, production and emissions data to create a unified intelligence layer for industrial performance and decarbonization.
Industrial systemsOperational signals from the infrastructure you already use
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Arvane intelligence layerContext, asset models, forecasting and optimization
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Connected decisionsProduction · Energy · Equipment · Cost · Carbon
EXISTING INFRASTRUCTURE → SHARED INTELLIGENCE
PRODUCTION / ENERGY / EQUIPMENT / COST / CARBONTHE ARVANE PERSPECTIVE
01THE OPERATING ENVIRONMENT
The data exists. The connections are missing.
A plant historian knows the process. A meter knows consumption. A maintenance system knows the asset’s service record. None tells the whole operating story alone.
SCADA, PLCs and DCS capture process behavior. MES and production databases record what was made. ERP, maintenance and utility systems hold the commercial and equipment context. Their timestamps, asset names and operating boundaries do not always align.
Arvane’s approach is to connect these records into an operational model: what happened, why it may have happened, what could happen next and which decisions deserve attention.
Energy intensityCarbon intensityEquipment performanceOperational decisionsCapital allocation
02 / ARVANE INTELLIGENCE LAYER
Align timestamps and engineering units, relate signals to assets and production states, and develop models against validated operating baselines. Missing or unreliable data must remain visible.
Integration scope, source systems and operating requirements are established for each industrial environment.
03CONNECTED CAPABILITIES
01 / CARBON & ENERGY INTELLIGENCE
Put consumption in its production context.
Energy becomes more useful when it can be traced to the equipment, process and output that required it.
Examine electricity and fuel consumption alongside production volumes, operating states and relevant emission factors. A site-level total can reveal a trend; a line- or asset-level view can help identify a decision.
OPERATIONAL HIERARCHY
01Enterprise
02Site
03Production area
04Production line
05Asset
06Process
07Product
The hierarchy defines the reporting boundary. Equipment can serve multiple processes and products, so allocation rules need to be explicit. Energy-related emissions and process emissions must remain distinguishable rather than being combined into an unexplained total.
THE DECISION
Which production area contributes to rising energy or carbon intensity, and which underlying operating conditions explain the change?
A fixed threshold can identify a high reading. A contextual baseline asks whether that reading is appropriate for the load, product, environment and equipment state.
Expected behavior is modelled from relevant operating history. Observed behavior is then compared under equivalent conditions to surface abnormal electricity use, excess fuel consumption, inefficient states, process drift and emerging degradation.
Expected400 kWh / cycle
Observed468 kWh / cycle
Deviation+17%
EXPECTED / OBSERVED / SAMPLE ASSET DATA
A deviation is a prompt to investigate. Meter quality, production changes and known maintenance events must be considered before an efficiency loss is assigned to equipment.
The lowest-energy strategy is not useful if it cannot meet production requirements.
Evaluate production targets alongside equipment capability, machine efficiency, energy prices, fuel availability and carbon intensity. Maintenance windows, renewable generation and storage availability may further shape the feasible operating space.
CONSTRAINED DECISION MODEL
MINIMIZEEnergy + Cost + Carbon
Objectives are weighted for the site. Different units are normalized before comparison.
Candidate strategies should make tradeoffs visible: moving a load could reduce energy cost while increasing carbon intensity, or improve efficiency while narrowing maintenance flexibility. The operations team sets the priorities and approves the response.
Combine historical time series with current conditions to estimate how demand and performance may evolve. Energy requirements, carbon intensity and equipment behavior should be forecast in relation to planned output, known operating changes and asset condition.
NEAR TERM
Prepare the next operating window.
Estimate energy demand and the implications of a changing production schedule.
DEVELOPING TREND
Investigate a changing asset.
Recognize patterns that could indicate emerging efficiency degradation or abnormal behavior.
PLANNING HORIZON
Test the operating assumptions.
Compare expected equipment performance and production requirements under alternative conditions.
A useful forecast includes its horizon, assumptions and uncertainty. Model suitability depends on available history, data quality and how closely future conditions resemble the conditions used for evaluation.
Equipment replacement, electrification, fuel switching, renewable generation, storage and heat recovery affect different parts of the operating system. Rescheduling production or changing operating parameters can also alter the investment case.
Scenario modelling creates a consistent basis for comparison: a defined baseline, an intervention, operating assumptions and a financial horizon. The purpose is to expose dependencies before capital is committed.
CAPEX
Capital, installation and integration requirements
Energy
Consumption, demand profile and energy source
Carbon
Energy and process boundaries, factor assumptions
Production
Throughput, quality, downtime and operating flexibility
Payback
Tariffs, utilization, maintenance and financial assumptions
